Algorithm 959: VBF: A Library of C plus plus Classes for Vector Boolean Functions in Cryptography

VBF is a collection of C++ classes designed for analyzing vector Boolean functions (functions that map a Boolean vector to another Boolean vector) from a cryptographic perspective. This implementation uses the NTL library from Victor Shoup, adding new modules that call NTL functions and complement the existing ones, making it better suited to cryptography. The class representing a vector Boolean function can be initialized by several alternative types of data structures such as Truth Table, Trace Representation, and Algebraic Normal Form (ANF), among others. The most relevant cryptographic criteria for both block and stream ciphers as well as for hash functions can be evaluated with VBF: it obtains the nonlinearity, linearity distance, algebraic degree, linear structures, and frequency distribution of the absolute values of the Walsh Spectrum or the Autocorrelation Spectrum, among others. In addition, operations such as equality testing, composition, inversion, sum, direct sum, bricklayering (parallel application of vector Boolean functions as employed in Rijndael cipher), and adding coordinate functions of two vector Boolean functions are presented. Finally, three real applications of the library are described: the first one analyzes the KASUMI block cipher, the second one analyzes the Mini-AES cipher, and the third one finds Boolean functions with very high nonlinearity, a key property for robustness against linear attacks

Cryptographic properties of Boolean functions defining elementary cellular automata

In this work, the algebraic properties of the local transition functions of elementary cellular automata (ECA) were analysed. Specifically, a classification of such cellular automata was done according to their algebraic degree, the balancedness, the resiliency, nonlinearity, the propagation criterion and the existence of non-zero linear structures. It is shown that there is not any ECA satisfying all properties at the same time

Random generation of Boolean functions with high degree of correlation immunity, Journal of Telecommunications and Information Technology, 2006, nr 3

In recent years a cryptographic community is paying a lot of attention to the constructions of so called resilient functions for use mainly in stream cipher systems. Very little work however has been devoted to random generation of such functions. This paper tries to fill that gap and presents an algorithm that can generate at random highly nonlinear resilient functions. Generated functions are analyzed and compared to the results obtained from the best know constructions and some upper bounds on nonlinearity and resiliency. It is shown that randomly generated functions achieve in most cases results equal to the best known designs, while in other cases fall just behind such constructs. It is argued that the algorithm can perhaps be used to prove the existence of some resilient functions for which no mathematical prove has been given so far

Construction of Balanced Boolean Functions with High Nonlinearity and Good Autocorrelation Properties

Boolean functions with high nonlinearity and good autocorrelation properties play an important role in the design of block ciphers and stream ciphers. In this paper, we give a method to construct balanced Boolean functions on $n$ variables, where $n\ge 10$ is an even integer, satisfying strict avalanche criterion (SAC). Compared with the known balanced Boolean functions with SAC property, the constructed functions possess the highest nonlinearity and the best global avalanche characteristics (GAC) property

Additive autocorrelation of some classes of cubic semi-bent Boolean functions

In this paper, we investigate the relation between the autocorrelation of a cubic Boolean function f\in \cB_n at a \in \BBF_{2^n} and the kernel of the bilinear form associated with $D_{a}f$, the derivative of $f$ at $a$. Further, we apply this technique to obtain the tight upper bounds of absolute indicator and sum-of-squares indicator for avalanche characteristics of various classes of highly nonlinear non-bent cubic Boolean functions

C-DIFFERENTIALS AND GENERALIZED CRYPTOGRAPHIC PROPERTIES OF VECTORIAL BOOLEAN AND P-ARY FUNCTIONS

This dissertation investigates a newly defined cryptographic differential, called a c-differential, and its relevance to the nonlinear substitution boxes of modern symmetric block ciphers. We generalize the notions of perfect nonlinearity, bentness, and avalanche characteristics of vectorial Boolean and p-ary functions using the c-derivative and a new autocorrelation function, while capturing the original definitions as special cases (i.e., when c=1). We investigate the c-differential uniformity property of the inverse function over finite fields under several extended affine transformations. We demonstrate that c-differential properties do not hold in general across equivalence classes typically used in Boolean function analysis, and in some cases change significantly under slight perturbations. Thus, choosing certain affine equivalent functions that are easy to implement in hardware or software without checking their c-differential properties could potentially expose an encryption scheme to risk if a c-differential attack method is ever realized. We also extend the c-derivative and c-differential uniformity into higher order, investigate some of their properties, and analyze the behavior of the inverse function's second order c-differential uniformity. Finally, we analyze the substitution boxes of some recognizable ciphers along with certain extended affine equivalent variations and document their performance under c-differential uniformity.Commander, United States NavyApproved for public release. Distribution is unlimited

Evolving balanced Boolean functions with optimal resistance to algebraic and fast algebraic attacks, maximal algebraic degree, and very high nonlinearity.

Using simulated annealing, we derive several equivalence classes of balanced Boolean functions with optimum algebraic immunity, fast algebraic resistance, and maximum possible algebraic degree. For numbers n of input bits less than 16, these functions also possess superior nonlinearity to all Boolean functions so far obtained with said properties

Some properties of q-ary functions based on spectral analysis

In this paper, we generalize some existing results on Boolean functions to the $q$-ary functions defined over \BBZ_q, where $q\geq 2$ is an integer, and obtain some new characterization of $q$-ary functions based on spectral analysis. We provide a relationship between Walsh-Hadamard spectra of two $p$-ary functions $f$ and $g$ (for $p$ a prime) and their derivative $D_{f, g}$. We provide a relationship between the Walsh-Hadamard spectra and the decompositions of any two $p$-ary functions. Further, we investigate a relationship between the Walsh-Hadamard spectra and the autocorrelation of any two $q$-ary functions

Additive Autocorrelation of Resilient Boolean Functions

Abstract. In this paper, we introduce a new notion called the dual func-tion for studying Boolean functions. First, we discuss general properties of the dual function that are related to resiliency and additive autocor-relation. Second, we look at preferred functions which are Boolean func-tions with the lowest 3-valued spectrum. We prove that if a balanced preferred function has a dual function which is also preferred, then it is resilient, has high nonlinearity and optimal additive autocorrelation. We demonstrate four such constructions of optimal Boolean functions using the Kasami, Dillon-Dobbertin, Segre hyperoval and Welch-Gong Transformation functions. Third, we compute the additive autocorrela-tion of some known resilient preferred functions in the literature by using the dual function. We conclude that our construction yields highly non-linear resilient functions with better additive autocorrelation than the Maiorana-McFarland functions. We also analysed the saturated func-tions, which are resilient functions with optimized algebraic degree and nonlinearity. We show that their additive autocorrelation have high peak values, and they become linear when we fix very few bits. These potential weaknesses have to be considered before we deploy them in applications.